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The simulated radar waveform generator is a software tool built with MATLAB to generate radar waveform datasets. The datasets can be used to develop and test detection algorithms for the 3.5 GHz CBRS or similar bands where the primary users of the band are federal incumbent radar systems. The software tool generates radar waveforms and randomizes the radar waveform parameters. The ranges of parameters values are selected based on NTIA testing procedures for ESC certification [1]. The parameters are pulse modulation, pulse duration, pulse repetition rate, chirp width and pulses per burst. In addition, we randomize the following parameters: start time, SNR, and the baseband center frequency of the radar signal.
This software consists of a MATLAB code and a graphical user interface (GUI). The MATLAB code generates simulated radar signals, mixes them with noise and manages the random generation of the parameters. The GUI simplifies the settings of the parameters and controls the dataset generation process.
- Current development environment: MATLAB 2019b
- The tool can be compiled and the executable can run without a MATLAB license. See the section How to run for more details
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Add the following folders to MATLAB path:
- \src\dsp\
- \src\util\
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At the MATLAB command prompt change the current directory to \src\app\ and run appdesigner('simulatedRadarWaveformGenerator.mlapp')
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Requires the following toolboxes to run all the functionalities:
- Signal Processing Toolbox
- 'DSP System Toolbox'
- 'Communications System Toolbox'
- 'Parallel Computing Toolbox'
- 'MATLAB Distributed Computing Server'
- 'Phased Array System Toolbox'
It is possible to compile an executable and run it in a machine without MATLAB installation or license. However, the licenses for MATLAB and the toolboxes are required during compilation.
To generate an executable for the software tool:
- Use either mcc see compileSimulatedRadarWG.m, or use the MATLAB deployment tool "deploytool".
- All necessary toolboxes are required during compilation in addition to the MATLAB Compiler.
For running a precompiled executable, MATLAB prerequisites for deployment must be installed. Instructions can be found in Matlab Prerequisites.
The first tab in the application is for generating waveforms. Five modulation bins are available. You can set the number of waveforms and the sampling frequency. The random waveform parameters are selected according to Table 1 in [1]. For P0N#2, the type of phase coding can also be changed.
After generating and saving a dataset of waveforms, we load the dataset folder in the second tab. We can set different parameters for the dataset as demonstrated in the figure below.
A reference RF dataset was generated using this software. The RF dataset is published at https://doi.org/10.18434/M32116. In addition a manual for using the GUI is available at Simulated Rada Waveform Generator GUI manual. The manual contains a glossary of the parameters that are used for generation. In addition, it includes the exact parameters that were used to generate the reference RF dataset. For more information see the additional references below.
Use the data record at https://data.nist.gov/od/id/mds2-2229 to cite this work.
- F. H. Sanders, J. E. Carroll, G. A. Sanders, R. L. Sole, J. S. Devereux, and E. F. Drocella, “Procedures for Laboratory Testing of Environmental Sensing Capability Sensor Devices,” National Telecommunications and Information Administration, Technical Memorandum TM 18-527, Nov. 2017. Available at http://www.its.bldrdoc.gov/publications/3184.aspx.
- R. Caromi, M. Souryal, T. Hall “RF Dataset of Incumbent Radar Signals in the 3.5 GHz CBRS Band,” Journal of Research (NIST JRES), Volume 124, Dec. 2019, available at https://www.nist.gov/publications/rf-dataset-incumbent-radar-signals-35-ghz-cbrs-band
- T. Hall, R. Caromi, M. Souryal, and A. Wunderlich, "Reference Datasets for Training and Evaluating RF Signal Detection and Classification Models," in Proc. IEEE GLOBECOM Workshop on Advancements in Spectrum Sharing, Dec. 2019, available at https://www.nist.gov/publications/reference-data-sets-training-and-evaluating-rf-signal-detection-and-classification